|
import parser |
|
import requests |
|
import re |
|
|
|
def zephyr_score(sentence): |
|
prompt = f"""<|user|> |
|
You are an assistant helping with paper reviews. |
|
You will be tasked to classify sentences as 'J' or 'V' |
|
|
|
'J' is positive or 'J' is encouraging. |
|
'J' has a neutral tone or 'J' is professional. |
|
'V' is overly blunt or 'V' contains excessive negativity and no constructive feedback. |
|
'V' contains an accusatory tone or 'V' contains sweeping generalizations or 'V' contains personal attacks. |
|
|
|
Text: "{sentence}" |
|
|
|
Please classify this text as either 'J' or 'V'. Only output 'J' or 'V' with no additional explanation.<|endoftext|> |
|
<|assistant|> |
|
""" |
|
return prompt |
|
|
|
def zephyr_revise(sentence): |
|
prompt = f"""<|user|> |
|
You are an assistant that helps users revise Paper Reviews. |
|
Paper reviews exist to provide authors of academic research papers constructive critism. |
|
|
|
This is text found in a review. |
|
This text was classified as 'toxic': |
|
|
|
Text: "{sentence}" |
|
|
|
Please revise this text such that it maintains the criticism in the original text and delivers it in a friendly but professional manner. Make minimal changes to the original text.<|endoftext|> |
|
<|assistant|> |
|
""" |
|
return prompt |
|
|
|
def mistral_score(sentence): |
|
prompt = f"""<s>[INST] |
|
You are an assistant helping with paper reviews. |
|
You will be tasked to classify sentences as 'J' or 'V' |
|
|
|
'J' is positive or 'J' is encouraging. |
|
'J' has a neutral tone or 'J' is professional. |
|
'V' is overly blunt or 'V' contains excessive negativity and no constructive feedback. |
|
'V' contains an accusatory tone or 'V' contains sweeping generalizations or 'V' contains personal attacks. |
|
|
|
Text: "{sentence}" |
|
|
|
Please classify this text as either 'J' or 'V'. Only output 'J' or 'V' with no additional explanation. [/INST]""" |
|
return prompt |
|
|
|
def mistral_revise(sentence): |
|
prompt = f"""<s>[INST] |
|
You are an assistant that helps users revise Paper Reviews. |
|
Paper reviews exist to provide authors of academic research papers constructive critism. |
|
|
|
This is text found in a review. |
|
This text was classified as 'toxic': |
|
|
|
Text: "{sentence}" |
|
|
|
Please revise this text such that it maintains the criticism in the original text and delivers it in a friendly but professional manner. Make minimal changes to the original text. [/INST] Revised Text: """ |
|
return prompt |
|
|
|
def phi_score(sentence): |
|
prompt = f"""<|system|> |
|
You are an assistant helping with paper reviews. |
|
You will be tasked to classify sentences as 'J' or 'V' |
|
|
|
'J' is positive or 'J' is encouraging. |
|
'J' has a neutral tone or 'J' is not discouraging and not encouraging. |
|
'V' is overly blunt or 'V' contains excessive negativity and no constructive feedback. |
|
'V' contains an accusatory tone or 'V' contains sweeping generalizations or 'V' contains personal attacks.<|end|> |
|
<|user|> |
|
Text: "{sentence}" |
|
|
|
Please classify this text as either 'J' or 'V'. Only output 'J' or 'V' with no additional explanation.<|end|> |
|
<|assistant|> |
|
""" |
|
return prompt |
|
|
|
def phi_revise(sentence): |
|
prompt = f"""<|system|> |
|
You are an assistant that helps users revise Paper Reviews. |
|
Paper reviews exist to provide authors of academic research papers constructive critism.<|end|> |
|
<|user|> |
|
This is text found in a review. |
|
This text was classified as 'toxic' |
|
|
|
Text: "{sentence}" |
|
|
|
Please revise this text such that it maintains the criticism in the original text and delivers it in a friendly but professional manner. Make minimal changes to the original text.<|end|> |
|
<|assistant|> |
|
""" |
|
return prompt |
|
|
|
def query_model_score(sentence, api_key, model_id, prompt_fun): |
|
API_URL = f"https://api-inference.huggingface.co/models/{model_id}" |
|
headers = {"Authorization": f"Bearer {api_key}"} |
|
prompt = prompt_fun(sentence) |
|
def query(payload): |
|
response = requests.post(API_URL, headers=headers, json=payload) |
|
return response.json() |
|
parameters = {"max_new_tokens" : 5, "temperature": 0.1, "return_full_text": False} |
|
options = {"wait_for_model": True} |
|
data = query({"inputs": f"{prompt}", "parameters": parameters, "options": options}) |
|
score = data[0]['generated_text'] |
|
if 'v' in score.lower(): |
|
return 1 |
|
else: |
|
return 0 |
|
|
|
def query_model_revise(sentence, api_key, model_id, prompt_fun): |
|
API_URL = f"https://api-inference.huggingface.co/models/{model_id}" |
|
headers = {"Authorization": f"Bearer {api_key}"} |
|
prompt = prompt_fun(sentence) |
|
def query(payload): |
|
response = requests.post(API_URL, headers=headers, json=payload) |
|
return response.json() |
|
parameters = {"max_new_tokens" : 200, "temperature": 0.1, "return_full_text": False} |
|
options = {"wait_for_model": True} |
|
data = query({"inputs": f"{prompt}", "parameters": parameters, "options": options}) |
|
revision = data[0]['generated_text'] |
|
return revision |
|
|
|
def extract_quotes(text): |
|
pattern = r'"([^"]*)"' |
|
matches = re.findall(pattern, text) |
|
if matches: |
|
return ' '.join(matches) |
|
else: |
|
return text |
|
|
|
def revise_review(review, api_key, model_id, highlight_color): |
|
result = { |
|
"success": False, |
|
"data": { |
|
"revision": "", |
|
"score": "", |
|
"sentence_count": "", |
|
"revised_sentences": "" |
|
}, |
|
"message": "" |
|
} |
|
|
|
if 'zephyr' in model_id.lower(): |
|
revision_prompt = zephyr_revise |
|
score_prompt = zephyr_score |
|
elif 'mistral' in model_id.lower(): |
|
revision_prompt = mistral_revise |
|
score_prompt = mistral_score |
|
elif 'phi' in model_id.lower(): |
|
revision_prompt = phi_revise |
|
score_prompt = phi_score |
|
|
|
try: |
|
review = review.replace('"', "'") |
|
sentences = parser.parse_sentences(review) |
|
review_score = 0 |
|
revision_count = 0 |
|
review_revision = "" |
|
for sentence in sentences: |
|
if len(sentence) > 10: |
|
score = query_model_score(sentence, api_key, model_id, score_prompt) |
|
if score == 0: |
|
review_revision += " " + sentence |
|
else: |
|
review_score = 1 |
|
revision_count +=1 |
|
revision = query_model_revise(sentence, api_key, model_id, revision_prompt) |
|
revision = extract_quotes(revision) |
|
revision = revision.strip().strip('"') |
|
review_revision += f" <div style='background-color: {highlight_color}; display: inline;'>{revision}</div>" |
|
else: |
|
review_revision += " " + sentence |
|
|
|
|
|
result["success"] = True |
|
result["message"] = "Review successfully revised!" |
|
result["data"]["revision"] = review_revision |
|
result["data"]["score"] = review_score |
|
result["data"]["sentence_count"] = sum(1 for sentence in sentences if len(sentence) > 20) |
|
result["data"]["revised_sentences"] = revision_count |
|
except Exception as e: |
|
result["message"] = str(e) |
|
|
|
return result |
|
|
